{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from keras.preprocessing.image import ImageDataGenerator, load_img\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "FAST_RUN = False\n",
    "IMAGE_WIDTH=128\n",
    "IMAGE_HEIGHT=128\n",
    "IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)\n",
    "IMAGE_CHANNELS=3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['tilt_03.jpg', 'fire_07.jpg', 'fire_03.jpg', 'ice_06.jpg', 'fire_17.jpg', 'fire_20.jpg', 'tilt_08.jpg', 'tilt_15.jpg', 'fire_16.jpg', 'fire_02.jpg', 'ice_19.jpg', 'tilt_01.jpg', 'fire_09.jpg', 'fire_12.jpg', 'ice_17.jpg', 'tilt_07.jpg', 'tilt_20.jpg', 'tilt_09.jpg', 'ice_07.jpg', 'ice_02.jpg', 'fire_13.jpg', 'fire_04.jpg', 'tilt_18.jpg', 'tilt_02.jpg', 'ice_10.jpg', 'tilt_04.jpg', 'fire_06.jpg', 'ice_16.jpg', 'tilt_13.jpg', 'fire_15.jpg', 'fire_10.jpg', 'ice_03.jpg', '.ipynb_checkpoints', 'tilt_05.jpg', 'fire_11.jpg', 'ice_12.jpg', 'ice_15.jpg', 'ice_08.jpg', 'fire_08.jpg', 'tilt_14.jpg', 'fire_19.jpg', 'ice_11.jpg', 'fire_05.jpg', 'tilt_16.jpg', 'ice_01.jpg', 'ice_09.jpg', 'ice_14.jpg', 'tilt_12.jpg', 'tilt_11.jpg', 'ice_04.jpg', 'tilt_06.jpg', 'fire_14.jpg', 'tilt_19.jpg', 'ice_13.jpg', 'fire_18.jpg', 'ice_20.jpg', 'ice_18.jpg', 'ice_05.jpg', 'tilt_10.jpg', 'fire_01.jpg', 'tilt_17.jpg']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(\"./dataset/train\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = os.listdir(\"./dataset/test\")\n",
    "categories = []\n",
    "for filename in filenames:\n",
    "    category = filename.split('_')[0]\n",
    "    if category == 'fire':\n",
    "        categories.append(1)\n",
    "    elif category == 'ice':\n",
    "        categories.append(0)\n",
    "    else:\n",
    "        categories.append(-1)\n",
    "\n",
    "df_test = pd.DataFrame({\n",
    "    'filename': filenames,\n",
    "    'category': categories\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f48bb343630>"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test['category'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = os.listdir(\"./dataset/train\")\n",
    "categories = []\n",
    "for filename in filenames:\n",
    "    category = filename.split('_')[0]\n",
    "    if category == 'fire':\n",
    "        categories.append(1)\n",
    "    elif category == 'ice':\n",
    "        categories.append(0)\n",
    "    else:\n",
    "        categories.append(-1)\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'filename': filenames,\n",
    "    'category': categories\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>tilt_03.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>fire_07.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>fire_03.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>ice_06.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>fire_17.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category     filename\n",
       "0        -1  tilt_03.jpg\n",
       "1         1  fire_07.jpg\n",
       "2         1  fire_03.jpg\n",
       "3         0   ice_06.jpg\n",
       "4         1  fire_17.jpg"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f491825a3c8>"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['category'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f49184103c8>"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test['category'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f48bb170cc0>"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = random.choice(filenames)\n",
    "image = load_img(\"./dataset/train/\"+sample)\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_16 (Conv2D)           (None, 126, 126, 32)      896       \n",
      "_________________________________________________________________\n",
      "batch_normalization_21 (Batc (None, 126, 126, 32)      128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_16 (MaxPooling (None, 63, 63, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_21 (Dropout)         (None, 63, 63, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 61, 61, 64)        18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_22 (Batc (None, 61, 61, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_17 (MaxPooling (None, 30, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_22 (Dropout)         (None, 30, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 28, 28, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_23 (Batc (None, 28, 28, 128)       512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_18 (MaxPooling (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_23 (Dropout)         (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_6 (Flatten)          (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 512)               12845568  \n",
      "_________________________________________________________________\n",
      "batch_normalization_24 (Batc (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dropout_24 (Dropout)         (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 3)                 1539      \n",
      "=================================================================\n",
      "Total params: 12,943,299\n",
      "Trainable params: 12,941,827\n",
      "Non-trainable params: 1,472\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(3, activation='softmax')) \n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping, ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystop = EarlyStopping(patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', \n",
    "                                            patience=2, \n",
    "                                            verbose=1, \n",
    "                                            factor=0.5, \n",
    "                                            min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [earlystop, learning_rate_reduction]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>filename</th>\n",
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       "      <th>0</th>\n",
       "      <td>-1</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    category     filename\n",
       "0         -1  tilt_26.jpg\n",
       "1          1  fire_23.jpg\n",
       "2          0   ice_23.jpg\n",
       "3          1  fire_25.jpg\n",
       "4          0   ice_26.jpg\n",
       "5          0   ice_21.jpg\n",
       "6         -1  tilt_25.jpg\n",
       "7          1  fire_24.jpg\n",
       "8          1  fire_26.jpg\n",
       "9          0   ice_25.jpg\n",
       "10         1  fire_22.jpg\n",
       "11         1  fire_21.jpg\n",
       "12        -1  tilt_24.jpg\n",
       "13         0   ice_22.jpg\n",
       "14        -1  tilt_22.jpg\n",
       "15        -1  tilt_23.jpg\n",
       "16        -1  tilt_21.jpg\n",
       "17         0   ice_24.jpg"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"category\"] = df[\"category\"].replace({1: 'fire', 0: 'ice', -1: 'tilt'}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, validate_df = train_test_split(df, test_size=0.25, random_state=42)\n",
    "train_df = train_df.reset_index(drop=True)\n",
    "validate_df = validate_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f48bb335898>"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df['category'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f48b9977828>"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEGCAYAAABB8K+FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAClJJREFUeJzt3F2I5YdZx/HfY7aFkEJMzRCNsG6LpYimsTpKlLQmxeBrrdXWCGJaX9gLi4pemPpW2lxYK9ILgy2srVC1VVqEqgRRSxNMbYNOkBiRCl60ohg6LSYxraKWx4uZvI0ze/5J58zZZ/fzgbBn5hxmfnByvvvf/3mp7g4AF74v2vQAAJYRbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYY4dZw/7Oqrr+4zZ84c548EuOjdf//9n+7urVW3O9ZgnzlzJjs7O8f5IwEuelX1ySW3c0oEYAjBBhhCsAGGEGyAIQQbYAjBBhhicbCr6h1V9cp1jgHgaIuCXVUvS/Kl3f0na94DwBFWBruqnpPkt5J8oqpetf5JABxmyTsdb0vyD0l+LclPVtXp7r7z8Sur6mySs0ly+vTptYw8ypk33nWiv++kfeJXv2vTE9brzVduesF6vfmRTS/gIrPklMhLk5zr7oeS/F6Sm596ZXef6+7t7t7e2lr5VngAnqUlwf6nJC/cv7ydZNF73gE4XktOibw7yW9X1Q8meU6S16x3EgCHWRns7v6PJK89gS0AnIc3zgAMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQwg2wBCCDTCEYAMMIdgAQ5w32FV1qqr+uaru2f/vupMaBsDTnVpx/UuS/H53334SYwA42qpTIjckeXVVfaSq3ltVqwIPwJqsCvbfJPmW7r4xycNJvvPgDarqbFXtVNXO7u7uOjYCkNXB/rvu/rf9yx9P8qKDN+juc9293d3bW1tbxz4QgD2rgv27VXV9VV2W5NVJHjiBTQAcYtU56TuSvC9JJfnj7v7Q+icBcJjzBru7/z57rxQBYMO8cQZgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYZYFOyquqaq/nbdYwA42tIj7F9Pcvk6hwBwfiuDXVWvSPLZJA+tfw4ARzl1viur6rlJ3pTke5N88IjbnE1yNklOnz593PuAC9B177lu0xPW6sHXPbjpCYdadYT9xiS/2d0PH3WD7j7X3dvdvb21tXW86wB4wqpgf2uSN1TVPUm+tqretf5JABzmvKdEuvvlj1+uqnu6+8fXPwmAwyx+HXZ337TGHQCs4I0zAEMINsAQgg0whGADDCHYAEMINsAQgg0whGADDCHYAEMINsAQgg0whGADDCHYAEMINsAQgg0whGADDCHYAEMINsAQgg0whGADDCHYAEMINsAQgg0whGADDCHYAEMsCnZVPb+qbqmqq9c9CIDDrQx2VX1ZkruSfGOSu6tqa+2rAPh/Ti24zVcn+Znuvq+qrkrydUn+bL2zADho5RF2d39oP9Yvz95R9sfWPwuAg5aew64ktyb5nySfP3Dd2araqaqd3d3dNUwEIFkY7N7zhiQfTfLdB647193b3b29teX0NsC6LHnS8faqum3/yy9O8vB6JwFwmCVH2OeS/HBV/WWSy5L8+XonAXCYla8S6e5/T3LLCWwB4Dy80xFgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYY4teoGVXVlkj/Yv+1jSW7t7v9e9zAAnm7JEfYPJXl7d9+S5KEk377eSQAcZuURdne/4ylfbiX51PrmAHCUxeewq+qbklzV3fcd+P7Zqtqpqp3d3d1jHwjAnkXBrqrnJ7kzyY8evK67z3X3dndvb21tHfc+APatDHZVPTfJ+5P8fHd/cv2TADjMkiPsH0vy9Ul+saruqapb17wJgEMsedLxnUneeQJbADgPb5wBGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYYQbIAhBBtgCMEGGEKwAYZYFOyquqaq7l33GACOtjLYVXVVkvckuWL9cwA4ypIj7M8nuTXJo2veAsB5rAx2dz/a3Y8cdX1Vna2qnara2d3dPd51ADzhC37SsbvPdfd2d29vbW0dxyYADuFVIgBDCDbAEIuD3d03rXEHACs4wgYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEEG2AIwQYYQrABhhBsgCEWBbuq3l1VH62qX1r3IAAOtzLYVfV9SS7r7m9Ocm1VvWj9swA4aMkR9k1J3r9/+cNJblzbGgCOdGrBba5I8q/7lx9N8pVPvbKqziY5u//lY1X1j8c374JzdZJPn9Qvq7ed1G+6ZJzo/Ze31In9qkvAyT72Xn/i991XLLnRkmA/luTy/cvPy4Gj8u4+l+TcM5o2VFXtdPf2pnfw7Lj/5nLf7VlySuT+PHka5Pokn1jbGgCOtOQI+4NJ7q2qa5N8R5Ib1jsJgMOsPMLu7kez98TjfUlu7u5H1j3qAnZJnPq5iLn/5nLfJanu3vQGABbwTkeAIQQbYAjBBhhCsLnoVdXXVNW3VdVXVdXzNr2HZ6+qLul3Wgv2M+CBP09V3ZnkLUnemuSFSd632UU8E1X1Fwe+9daNDLlALHkdNnnigX9tkhck+eUkb0vyPRsdxRLXdfdNVfXh7r6rqn5u04NYrapekuSlSb68qm7b//YVSf5rc6s2zxH2ctd19/cnebi770py5aYHschuVb0pyVVV9bokD216EIvUIX9+JskPbGbOhcER9nIe+DPdlr0PJ/tY9v6S/ZHNzmGJ7n4gyQNV9eLu/p1N77lQeOPMQlV1efYe+C9O8vEk7+ruz212FXApcUpkuU7yV939E0k+m+R/N7yHBarqTze9AY6LUyLLvT/JHybZSXJNkvcmee1GF7HEg1X1qu7+o00PYbmqujt7B0lP+3aS7u5XbGDSBcEpkYWq6t7uftlTvr67u2/e5CZW23/g35Dkwez9y+iSfsAzmyPs5f6lqm5P8tdJviHJpza8hwX8pcrFxDns5V6f5HNJXpPkP7P36gOAE+OUCMAQTolwUaqqt3f3zx548uqSf9JqCvff4Rxhr+B/HOBCIdhclB5/KV9VfUl3f2bTezgeVXVjd39k0zs2xZOOXKx+ev/PD2x0BV8Qn9b3dM5hc7HqqrojyQv2PwPmySu679jQJhbyaX2HE2wuVq9Ocn2SVya5J09+6hsz+LS+QziHzUWtqn6qu39j0zt4dqrqV7r7Fza940Ih2ABDeNIRYAjBBhhCsAGGEGyAIQQbYIj/A3kV9zpQqNaFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "validate_df['category'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_train = train_df.shape[0]\n",
    "total_validate = validate_df.shape[0]\n",
    "batch_size=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    rotation_range=15,\n",
    "    rescale=1./255,\n",
    "    shear_range=0.1,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 44 validated image filenames belonging to 3 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/keras_preprocessing/image/dataframe_iterator.py:273: UserWarning: Found 1 invalid image filename(s) in x_col=\"filename\". These filename(s) will be ignored.\n",
      "  .format(n_invalid, x_col)\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_dataframe(\n",
    "    train_df, \n",
    "    \"./dataset/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='category',\n",
    "    target_size=IMAGE_SIZE,\n",
    "    class_mode='categorical',\n",
    "    batch_size=batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 16 validated image filenames belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "validation_generator = validation_datagen.flow_from_dataframe(\n",
    "    validate_df, \n",
    "    \"./dataset/train/\", \n",
    "    x_col='filename',\n",
    "    y_col='category',\n",
    "    target_size=IMAGE_SIZE,\n",
    "    class_mode='categorical',\n",
    "    batch_size=batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "9/9 [==============================] - 6s 626ms/step - loss: 0.8306 - accuracy: 0.8409 - val_loss: 10.4806 - val_accuracy: 0.4000\n",
      "Epoch 2/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/keras/callbacks/callbacks.py:1042: RuntimeWarning: Reduce LR on plateau conditioned on metric `val_acc` which is not available. Available metrics are: val_loss,val_accuracy,loss,accuracy,lr\n",
      "  (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 5s 600ms/step - loss: 0.6764 - accuracy: 0.8409 - val_loss: 4.8433 - val_accuracy: 0.2727\n",
      "Epoch 3/50\n",
      "9/9 [==============================] - 5s 610ms/step - loss: 0.5549 - accuracy: 0.8409 - val_loss: 3.0495 - val_accuracy: 0.6364\n",
      "Epoch 4/50\n",
      "9/9 [==============================] - 5s 589ms/step - loss: 0.4874 - accuracy: 0.8182 - val_loss: 14.5022 - val_accuracy: 0.1818\n",
      "Epoch 5/50\n",
      "9/9 [==============================] - 5s 610ms/step - loss: 1.2348 - accuracy: 0.6818 - val_loss: 4.7085 - val_accuracy: 0.3333\n",
      "Epoch 6/50\n",
      "9/9 [==============================] - 5s 600ms/step - loss: 0.3014 - accuracy: 0.8864 - val_loss: 6.0543 - val_accuracy: 0.3636\n",
      "Epoch 7/50\n",
      "9/9 [==============================] - 5s 599ms/step - loss: 0.6148 - accuracy: 0.7500 - val_loss: 4.9929 - val_accuracy: 0.5455\n",
      "Epoch 8/50\n",
      "9/9 [==============================] - 5s 600ms/step - loss: 0.3205 - accuracy: 0.8409 - val_loss: 13.3228 - val_accuracy: 0.2727\n",
      "Epoch 9/50\n",
      "9/9 [==============================] - 5s 599ms/step - loss: 0.2548 - accuracy: 0.9091 - val_loss: 8.1915 - val_accuracy: 0.4000\n",
      "Epoch 10/50\n",
      "9/9 [==============================] - 5s 600ms/step - loss: 0.3827 - accuracy: 0.9091 - val_loss: 7.1797 - val_accuracy: 0.3636\n",
      "Epoch 11/50\n",
      "9/9 [==============================] - 5s 587ms/step - loss: 1.2202 - accuracy: 0.6818 - val_loss: 4.5116 - val_accuracy: 0.4545\n",
      "Epoch 12/50\n",
      "9/9 [==============================] - 5s 610ms/step - loss: 0.7275 - accuracy: 0.8182 - val_loss: 0.9416 - val_accuracy: 0.2727\n",
      "Epoch 13/50\n",
      "9/9 [==============================] - 5s 600ms/step - loss: 0.7666 - accuracy: 0.7500 - val_loss: 2.5509 - val_accuracy: 0.3333\n",
      "Epoch 14/50\n",
      "9/9 [==============================] - 5s 599ms/step - loss: 0.4993 - accuracy: 0.7955 - val_loss: 6.1365 - val_accuracy: 0.4545\n",
      "Epoch 15/50\n",
      "9/9 [==============================] - 5s 599ms/step - loss: 0.6191 - accuracy: 0.7727 - val_loss: 8.1018 - val_accuracy: 0.2727\n",
      "Epoch 16/50\n",
      "9/9 [==============================] - 5s 590ms/step - loss: 0.6868 - accuracy: 0.7955 - val_loss: 1.0774 - val_accuracy: 0.4545\n",
      "Epoch 17/50\n",
      "9/9 [==============================] - 5s 610ms/step - loss: 0.6280 - accuracy: 0.8182 - val_loss: 4.9291 - val_accuracy: 0.3333\n",
      "Epoch 18/50\n",
      "9/9 [==============================] - 5s 610ms/step - loss: 0.7978 - accuracy: 0.7727 - val_loss: 2.4631 - val_accuracy: 0.6364\n",
      "Epoch 19/50\n",
      "9/9 [==============================] - 5s 588ms/step - loss: 0.1608 - accuracy: 0.9318 - val_loss: 6.0624 - val_accuracy: 0.1818\n",
      "Epoch 20/50\n",
      "9/9 [==============================] - 5s 601ms/step - loss: 0.1871 - accuracy: 0.9545 - val_loss: 2.6581e-04 - val_accuracy: 0.5455\n",
      "Epoch 21/50\n",
      "9/9 [==============================] - 6s 643ms/step - loss: 0.9792 - accuracy: 0.7273 - val_loss: 1.5479 - val_accuracy: 0.4667\n",
      "Epoch 22/50\n",
      "9/9 [==============================] - 5s 599ms/step - loss: 0.7407 - accuracy: 0.7955 - val_loss: 3.0309 - val_accuracy: 0.4545\n",
      "Epoch 23/50\n",
      "9/9 [==============================] - 5s 600ms/step - loss: 0.5261 - accuracy: 0.8182 - val_loss: 2.4653 - val_accuracy: 0.4545\n",
      "Epoch 24/50\n",
      "9/9 [==============================] - 5s 601ms/step - loss: 0.6580 - accuracy: 0.7500 - val_loss: 1.2158 - val_accuracy: 0.2727\n",
      "Epoch 25/50\n",
      "9/9 [==============================] - 5s 609ms/step - loss: 0.5391 - accuracy: 0.8636 - val_loss: 3.3490 - val_accuracy: 0.4667\n",
      "Epoch 26/50\n",
      "9/9 [==============================] - 6s 611ms/step - loss: 0.7930 - accuracy: 0.7955 - val_loss: 1.1488 - val_accuracy: 0.5455\n",
      "Epoch 27/50\n",
      "9/9 [==============================] - 5s 599ms/step - loss: 0.8015 - accuracy: 0.7500 - val_loss: 1.2829 - val_accuracy: 0.6364\n",
      "Epoch 28/50\n",
      "9/9 [==============================] - 5s 600ms/step - loss: 0.1800 - accuracy: 0.9773 - val_loss: 0.7113 - val_accuracy: 0.6364\n",
      "Epoch 29/50\n",
      "9/9 [==============================] - 5s 609ms/step - loss: 0.4475 - accuracy: 0.7955 - val_loss: 0.5503 - val_accuracy: 0.8000\n",
      "Epoch 30/50\n",
      "9/9 [==============================] - 5s 600ms/step - loss: 0.4901 - accuracy: 0.8636 - val_loss: 0.6769 - val_accuracy: 0.5455\n"
     ]
    }
   ],
   "source": [
    "epochs=3 if FAST_RUN else 50\n",
    "history = model.fit_generator(\n",
    "    train_generator, \n",
    "    epochs=epochs,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=total_validate//batch_size,\n",
    "    steps_per_epoch=total_train//batch_size,\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(12, 12))\n",
    "\n",
    "ax.plot(history.history['accuracy'], color='b', label=\"Training accuracy\")\n",
    "ax.set_xticks(np.arange(1, epochs, 1))\n",
    "ax.set_ylim(0,1)\n",
    "\n",
    "legend = plt.legend(loc='best', shadow=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_filenames = os.listdir(\"./dataset/test/\")\n",
    "test_df = pd.DataFrame({\n",
    "    'filename': test_filenames\n",
    "})\n",
    "nb_samples = test_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 18 validated image filenames.\n"
     ]
    }
   ],
   "source": [
    "test_gen = ImageDataGenerator(rescale=1./255)\n",
    "test_generator = test_gen.flow_from_dataframe(\n",
    "    test_df, \n",
    "    \"./dataset/test/\", \n",
    "    x_col='filename',\n",
    "    y_col=None,\n",
    "    class_mode=None,\n",
    "    target_size=IMAGE_SIZE,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict_generator(test_generator, steps=np.ceil(nb_samples/batch_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.3910952e-03, 1.5504862e-02, 9.8310411e-01],\n",
       "       [9.9554187e-01, 4.4137319e-03, 4.4413289e-05],\n",
       "       [9.3463393e-07, 8.8024177e-03, 9.9119663e-01],\n",
       "       [8.6530012e-01, 1.3441919e-01, 2.8066887e-04],\n",
       "       [4.0565410e-05, 7.3204958e-01, 2.6790982e-01],\n",
       "       [8.3825447e-02, 7.3716924e-02, 8.4245759e-01],\n",
       "       [1.8476161e-05, 1.8320032e-04, 9.9979836e-01],\n",
       "       [9.7587353e-01, 2.1926069e-03, 2.1933798e-02],\n",
       "       [7.0572335e-01, 8.8681160e-03, 2.8540856e-01],\n",
       "       [2.0526795e-06, 8.8488734e-01, 1.1511065e-01],\n",
       "       [9.8855805e-01, 1.1436413e-02, 5.5972032e-06],\n",
       "       [9.3478197e-01, 6.5195806e-02, 2.2283879e-05],\n",
       "       [3.2777411e-05, 5.2050082e-03, 9.9476224e-01],\n",
       "       [3.0798186e-04, 9.1758990e-01, 8.2102045e-02],\n",
       "       [1.9835904e-02, 2.8580297e-02, 9.5158380e-01],\n",
       "       [2.5992467e-05, 3.0071385e-05, 9.9994397e-01],\n",
       "       [1.9218837e-04, 6.2462902e-03, 9.9356151e-01],\n",
       "       [1.4811799e-06, 7.2829621e-03, 9.9271554e-01]], dtype=float32)"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['category'] = np.argmax(predict, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tilt_26.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fire_23.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ice_23.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fire_25.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ice_26.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ice_21.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tilt_25.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fire_24.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fire_26.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ice_25.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fire_22.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>fire_21.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>tilt_24.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ice_22.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>tilt_22.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>tilt_23.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>tilt_21.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ice_24.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       filename  category\n",
       "0   tilt_26.jpg         2\n",
       "1   fire_23.jpg         0\n",
       "2    ice_23.jpg         2\n",
       "3   fire_25.jpg         0\n",
       "4    ice_26.jpg         1\n",
       "5    ice_21.jpg         2\n",
       "6   tilt_25.jpg         2\n",
       "7   fire_24.jpg         0\n",
       "8   fire_26.jpg         0\n",
       "9    ice_25.jpg         1\n",
       "10  fire_22.jpg         0\n",
       "11  fire_21.jpg         0\n",
       "12  tilt_24.jpg         2\n",
       "13   ice_22.jpg         1\n",
       "14  tilt_22.jpg         2\n",
       "15  tilt_23.jpg         2\n",
       "16  tilt_21.jpg         2\n",
       "17   ice_24.jpg         2"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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